Spiking Neural Networks use the precise timing of action potentials to convey meaning. The conduction delays between neurons are one set of parameters that can be tuned to improve network performance on computational tasks, however no biologically inspired delay learning rules have been adopted by the artificial neural network community. This work shows the computational properties of delay update rules that are based on how delay change in living neural networks, as well as how the actual biological data can be used to improve performance for a prediction task.

Biologically Inspired Artificial Neural Networks, such as Spiking Neural Networks (SNNs), promise to provide significant advances over classic Artificial Neural Networks (ANNs) by performing computations in ways similar to the living brain. SNNs use discrete action potentials, which require a finite amount of time to travel between neurons. Most SNNs assume this axonal conduction delay to be constant, in spite of growing biological evidence that this conduction delay shows both long term and short term plasticity. We are working to explore the computational implications of these dynamics.

BIANN uses biologically plausible spiking neuron models. It bridges the gap between oversimplified ANNs and living neural networks. Effective encoding, decoding and training mechanisms for BIANN still need to be developed. A reservoir computing based training approach is proposed for the BIANN to serve as a novel modeling and control tool for practical applications. The BIANN is able to provide accurate one and five steps-ahead predictions of the rotor speed and terminal voltage of a generator in a SMIB, for online monitoring purposes.

Dish-Stirling systems are a form of concentrating solar power (CSP) emerging as an efficient and reliable source of renewable energy. Various technical hurdles are involved in the grid interconnection of dish-Stirling systems, particularly with issues related to power factor correction, low voltage ride-through capability, and reactive power planning. While there are no gridinterconnection requirements specific to dish-Stirling technology, the requirements currently established for wind farms are used as a starting point due to the similar design and operating characteristics between wind farms and dish-Stirling solar farms. A dish-Stirling solar farm requires external reactive power compensation to meet the power factor requirements presently set for wind farms.

Wind turbine power curves are based on the industry standard IEC 61400-12-1. Power curves are used for planning purposes and estimating total wind power production. Wind velocity are collected and averaged over 10-minute periods. Traditional methods do not explain varying characteristics in wind dynamics where multiple power productions are observed for same wind speed. When the input parameters such as wind speed and wind directions are known and the output parameter wind power are known for an installed wind turbine generation plant, a dynamic computational network such as neural network is used to develop operation model and estimate the wind power generation.

The micro-grid will be an important part of future power system. It contains the typical elements in present and future power systems and also contains some renewable energy sources, eg. wind power, photovoltaic energy, and energy storage. Power electronic devices/converters act as interface between the renewable energy sources and the power grid. An intelligent controller will be necessary to ensure stability of the micro-grid.

Smart grid consists of conventional generations, wind, solar and gridable vehicles (GVs). Intelligent optimization methods result in reduction of cost of energy and emission. GVs operate in two modes: grid-to-vehicle (G2V, loads and storage), and vehicle-to-grid (V2G, sources). “Smartparks with GVs” are as virtual power plants consisting of several small portable power plants (vehicles).

V2G power transactions are going to be an integrated part of the smart grid. This study shows how sudden charging and discharging of the SmartParks will impact the power system stability and demonstrates the potential of a wide-area controller to mitigate the impacts.

An adaptive, optimal, real-time controller based on adaptive critics design called dynamic stochastic optimal power flow (DSOPF) controller is proposed. Stochastic nature in power system can arise as a result of load and generation stochastic behaviors and due to random noise in PMU data which arises due to communication noise and measurement error. DSOPF controller can perform real-time control action but system wide information cannot be made available to DSOPF controller in real-time because of power system communication delays which can range from a few milliseconds to several seconds depending on distance and communication media.

If state variables can be predicted ahead of time, then communication delay can be compensated for. Hence, a scalable wide area monitoring system that can predict state variables ahead of time is developed. Scalability is achieved by using cellular architecture called cellular computational network (CCN). This module can effectively compensate for communication delays and hence can enable DSOPF controller to perform real-time control with system wide information.